DeepAI AI Chat
Log In Sign Up

Comparison of Quality Indicators in User-generated Content Using Social Media and Scholarly Text

10/24/2019
by   Manirupa Das, et al.
0

Predicting the quality of a text document is a critical task when presented with the problem of measuring the performance of a document before its release. In this work, we evaluate various features including those extracted from the text content (textual) and those describing higher-level characteristics of the text (meta) features that are not directly available from the text, and show how these features inform prediction of document quality in different ways. Moreover, we also compare our methods on both social user-generated data such as tweets, and scholarly user-generated data such as academic articles, showing how the same features differently influence prediction of quality across these disparate domains.

READ FULL TEXT

page 1

page 2

page 3

page 4

01/04/2019

A Joint Model for Multimodal Document Quality Assessment

The quality of a document is affected by various factors, including gram...
03/16/2017

What makes papers visible on social media? An analysis of various document characteristics

In this study we have investigated the relationship between different do...
09/02/2021

Coordinating Narratives and the Capitol Riots on Parler

Coordinated disinformation campaigns are used to influence social media ...
08/17/2017

Automatic Organisation and Quality Analysis of User-Generated Content with Audio Fingerprinting

The increase of the quantity of user-generated content experienced in so...
05/02/2019

KnowBias: A Novel AI Method to Detect Polarity in Online Content

We introduce KnowBias, a system for detecting the degree of political bi...
10/05/2019

Content-Based Features to Rank Influential Hidden Services of the Tor Darknet

The unevenness importance of criminal activities in the onion domains of...
08/13/2012

Detecting Events and Patterns in Large-Scale User Generated Textual Streams with Statistical Learning Methods

A vast amount of textual web streams is influenced by events or phenomen...